Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.

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3D measurement of the shape of rough structures can be realised with structured light illumination techniques. Several problems can arise while measuring complex object geometries with these techniques. Complex objects are characterized, f.e. by deep holes, walls, concave and convex corner-like shaped surface structures. When illuminating the object, one part of the object can "illuminate" another one, yielding locally spurious fringe patterns. Due to these spurious fringe patterns the phase values are strongly distorted significantly increasing the measurement noise locally. Here we propose methods how to detect and to avoid these spurious fringe patterns. The idea is to use the overestimated information which is contained in the graycode and the sinusoidal intensity distribution. On the basis of this procedure, an operator is defined which results in a mask operation. With this new method we can reduce the noise amplitude. In this paper, the detection and reduction of the illumination effect using this operator will be demonstrated while measuring different object geometries

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